Namespaces

Types in MathNet.Numerics.Distributions

Type Normal

Namespace MathNet.Numerics.Distributions

Interfaces IContinuousDistribution

Continuous Univariate Normal distribution, also known as Gaussian distribution. For details about this distribution, see.

Constructors

Static Functions

Methods

Properties

Public Constructors

Normal(double mean, double stddev, Random randomSource)

Initializes a new instance of the Normal class with a particular mean and standard deviation. The distribution will be initialized with the default random number generator.
Parameters
double mean

The mean (μ) of the normal distribution.

double stddev

The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.

Random randomSource

The random number generator which is used to draw random samples.

Normal(Random randomSource)

Initializes a new instance of the Normal class. This is a normal distribution with mean 0.0 and standard deviation 1.0. The distribution will be initialized with the default random number generator.
Parameters
Random randomSource

The random number generator which is used to draw random samples.

Normal()

Initializes a new instance of the Normal class. This is a normal distribution with mean 0.0 and standard deviation 1.0. The distribution will be initialized with the default random number generator.

Normal(double mean, double stddev)

Initializes a new instance of the Normal class with a particular mean and standard deviation. The distribution will be initialized with the default random number generator.
Parameters
double mean

The mean (μ) of the normal distribution.

double stddev

The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.

Public Static Functions

double CDF(double mean, double stddev, double x)

Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
MATLAB: normcdf
Parameters
double mean

The mean (μ) of the normal distribution.

double stddev

The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.

double x

The location at which to compute the cumulative distribution function.

Return
double

the cumulative distribution at location x.

Normal Estimate(IEnumerable<double> samples, Random randomSource)

Estimates the normal distribution parameters from sample data with maximum-likelihood.
MATLAB: normfit
Parameters
IEnumerable<double> samples

The samples to estimate the distribution parameters from.

Random randomSource

The random number generator which is used to draw random samples. Optional, can be null.

Return
Normal

A normal distribution.

double InvCDF(double mean, double stddev, double p)

Computes the inverse of the cumulative distribution function (InvCDF) for the distribution at the given probability. This is also known as the quantile or percent point function.
MATLAB: norminv
Parameters
double mean

The mean (μ) of the normal distribution.

double stddev

The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.

double p

The location at which to compute the inverse cumulative density.

Return
double

the inverse cumulative density at p.

bool IsValidParameterSet(double mean, double stddev)

Tests whether the provided values are valid parameters for this distribution.
Parameters
double mean

The mean (μ) of the normal distribution.

double stddev

The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.

double PDF(double mean, double stddev, double x)

Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
MATLAB: normpdf
Parameters
double mean

The mean (μ) of the normal distribution.

double stddev

The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.

double x

The location at which to compute the density.

Return
double

the density at x.

double PDFLn(double mean, double stddev, double x)

Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
Parameters
double mean

The mean (μ) of the normal distribution.

double stddev

The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.

double x

The location at which to compute the density.

Return
double

the log density at x.

double Sample(Random rnd, double mean, double stddev)

Generates a sample from the normal distribution using the algorithm.
Parameters
Random rnd

The random number generator to use.

double mean

The mean (μ) of the normal distribution.

double stddev

The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.

Return
double

a sample from the distribution.

double Sample(double mean, double stddev)

Generates a sample from the normal distribution using the algorithm.
Parameters
double mean

The mean (μ) of the normal distribution.

double stddev

The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.

Return
double

a sample from the distribution.

IEnumerable<double> Samples(Random rnd, double mean, double stddev)

Generates a sequence of samples from the normal distribution using the algorithm.
Parameters
Random rnd

The random number generator to use.

double mean

The mean (μ) of the normal distribution.

double stddev

The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.

Return
IEnumerable<double>

a sequence of samples from the distribution.

void Samples(Random rnd, Double[] values, double mean, double stddev)

Fills an array with samples generated from the distribution.
Parameters
Random rnd

The random number generator to use.

Double[] values

The array to fill with the samples.

double mean

The mean (μ) of the normal distribution.

double stddev

The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.

Return
void

a sequence of samples from the distribution.

IEnumerable<double> Samples(double mean, double stddev)

Generates a sequence of samples from the normal distribution using the algorithm.
Parameters
double mean

The mean (μ) of the normal distribution.

double stddev

The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.

Return
IEnumerable<double>

a sequence of samples from the distribution.

void Samples(Double[] values, double mean, double stddev)

Fills an array with samples generated from the distribution.
Parameters
Double[] values

The array to fill with the samples.

double mean

The mean (μ) of the normal distribution.

double stddev

The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.

Return
void

a sequence of samples from the distribution.

Normal WithMeanPrecision(double mean, double precision, Random randomSource)

Constructs a normal distribution from a mean and precision.
Parameters
double mean

The mean (μ) of the normal distribution.

double precision

The precision of the normal distribution.

Random randomSource

The random number generator which is used to draw random samples. Optional, can be null.

Return
Normal

A normal distribution.

Normal WithMeanStdDev(double mean, double stddev, Random randomSource)

Constructs a normal distribution from a mean and standard deviation.
Parameters
double mean

The mean (μ) of the normal distribution.

double stddev

The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.

Random randomSource

The random number generator which is used to draw random samples. Optional, can be null.

Return
Normal

a normal distribution.

Normal WithMeanVariance(double mean, double var, Random randomSource)

Constructs a normal distribution from a mean and variance.
Parameters
double mean

The mean (μ) of the normal distribution.

double var

The variance (σ^2) of the normal distribution.

Random randomSource

The random number generator which is used to draw random samples. Optional, can be null.

Return
Normal

A normal distribution.

Public Methods

double CumulativeDistribution(double x)

Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
Parameters
double x

The location at which to compute the cumulative distribution function.

Return
double

the cumulative distribution at location x.

double Density(double x)

Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
Parameters
double x

The location at which to compute the density.

Return
double

the density at x.

double DensityLn(double x)

Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
Parameters
double x

The location at which to compute the log density.

Return
double

the log density at x.

bool Equals(object obj)

int GetHashCode()

Type GetType()

double InverseCumulativeDistribution(double p)

Computes the inverse of the cumulative distribution function (InvCDF) for the distribution at the given probability. This is also known as the quantile or percent point function.
Parameters
double p

The location at which to compute the inverse cumulative density.

Return
double

the inverse cumulative density at p.

double Sample()

Generates a sample from the normal distribution using the algorithm.
Return
double

a sample from the distribution.

void Samples(Double[] values)

Fills an array with samples generated from the distribution.

IEnumerable<double> Samples()

Generates a sequence of samples from the normal distribution using the algorithm.
Return
IEnumerable<double>

a sequence of samples from the distribution.

string ToString()

A string representation of the distribution.
Return
string

a string representation of the distribution.

Public Properties

double Entropy get;

Gets the entropy of the normal distribution.

double Maximum get;

Gets the maximum of the normal distribution.

double Mean get;

Gets the mean (μ) of the normal distribution.

double Median get;

Gets the median of the normal distribution.

double Minimum get;

Gets the minimum of the normal distribution.

double Mode get;

Gets the mode of the normal distribution.

double Precision get;

Gets the precision of the normal distribution.

Random RandomSource get; set;

Gets the random number generator which is used to draw random samples.

double Skewness get;

Gets the skewness of the normal distribution.

double StdDev get;

Gets the standard deviation (σ) of the normal distribution. Range: σ ≥ 0.

double Variance get;

Gets the variance of the normal distribution.